rANOMALY use case
Etienne Rifa & Sebastien Theil
17/08/2020
1 rANOMALY step-by-step use case.
1.1 Help
Each function have a detailed help accessible in R via ?{funtion}.
1.2 Tests datasets
The dataset can be downloaded via this link.
This tutorial assume that you have extracted all the read file in a folder named reads along with the sample-metadata.csv file.
We share a 24 samples test dataset extract from rats feces at two different time (t0 & t50) and in two nutrition conditions. Also included two extraction control sample (blank).
sm <- read.table("sample_metadata.csv", sep="\t",header=TRUE)
DT::datatable(sm)load("decontam_out/robjects.Rdata")1.3 ASV definition with DADA2
The first step will be the creation of ASVs (Amplicon Sequence Variants) thanks to the dada2 package. In rANOMALY, only one function is needed to compute all the different steps require from this package.
Sample names will be extracted from the file name, so files must be formatted as followed : {sample-id1}_R1.fastq.gz {sample-id1}_R2.fastq.gz etc…
dada_res = dada2_fun(path="./reads", dadapool = "pseudo", compress=TRUE, plot=FALSE)Main output: - read_tracking.csv that summarize the read number after each filtering step.
DT::datatable(read.table("dada2_out/read_tracking.csv",sep="\t",header=TRUE))The sample names extracted from the file name. We consider as sample name anything that is before the first underscore. This must match the sample names that are in sample metadata files. input: raw read number. filtered: after dada2 filtering step: no N’s in sequence, low quality, and phiX. denoisedF & denoisedR: after denoising. Forward & Reverse. merged: after merging R1 & R2. nonchim: after chimeras filtering.
dada2_robjects.Rdatawith raw ASV table and representative sequences in objectsotu.table,seqtab.export&seqtab.nochim.raw_asv-table.csvrep-seqs.fna
1.4 Taxonomic assignment
This function uses IDTAXA function from DECIPHER package, and allows to use 2 differents databases. It keeps the best assignation on 2 criteria, resolution (depth) and confidence. The final taxonomy is validated by multiple ancestors taxa and incongruity correction step.
We share the latest databases we use in the IDTAXA format in this link. You can also generate your own database following those instructions and scripts we provide in another repository.
tax.table = assign_taxo_fun(dada_res = dada_res, id_db = c("path_to_your_banks/silva/SILVA_SSU_r132_March2018.RData","path_to_your_banks/DAIRYdb_v1.2.0_20190222_IDTAXA.RData") )Main output: - taxo_robjects.Rdata with taxonomy in phyloseq format in tax.table object. - final_tax_table.csv the final assignation table that will be use in next steps. - allDB_tax_table.csv raw assignations from the two databases, mainly for debugging.
1.5 Phylogenetic Tree
The phylogenetic tree from the representative sequences is generated using phangorn and DECIPHER packages.
tree = generate_tree_fun(dada_res)Main output: - tree_robjects.Rdata with phylogenetic tree object in phyloseq format.
1.6 Phyloseq object
To create a phyloseq object, we need to merge four objects and one file: - the asv table otu.table and the representative sequences seqtab.nochim from dada2_robjects.Rdata - a taxonomy table taxo_robjects.Rdata from taxo_robjects.Rdata - the phylogenetic tree tree from tree_robjects.Rdata - metadata from sample-metadata.csv
data = generate_phyloseq_fun(dada_res = dada_res, taxtable = tax.table, tree = tree, metadata = "./sample_metadata.csv")Main output: - robjects.Rdata with phyloseq object in data for raw counts and data_rel for relative abundance.
1.7 Decontamination
The decontam_fun function uses decontam R package with control samples to filter contaminants. The decontam package offers two main methods, frequency and prevalence (and then you can combine those methods). For frequency method, it is mandatory to have the dna concentration of each sample in phyloseq (and hence in the sample-metadata.csv). “In this method, the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.” In the prevalence methods no need of DNA quantification. “In this method, the prevalence (presence/absence across samples) of each sequence feature in true positive samples is compared to the prevalence in negative controls to identify contaminants.”
Tips: sequencing plateforms often quantify the DNA before sequencing, but do not automaticaly give the information. Just ask for it ;).
Our function integrates the basics ASV frequency (nb_reads_ASV/nb_total_reads) and prevalence (nb_sample_ASV/nb_total_sample) filtering. As in our lab we had a known recurrent contaminant we included an option to filter out ASV based on they taxa names.
data = decontam_fun(data = data, domain = "Bacteria", column = "type", ctrl_identifier = "control", spl_identifier = "sample", number = 100)Main output: - robjects.Rdata with contaminant filtered phyloseq object named data. - Exclu_out.csv list of filtered ASVs for each filtering step. - Kronas before and after filtering. - raw_asv-table.csv & relative_asv-table.csv. - venndiag_filtering.png.
venndiag
1.8 Plots, diversity and statistics
!!! We are currently developping a ShinyApp to visualize your data, sub-select your samples/taxons and do all those analyses interactively !!! ExploreMetabar
1.8.1 Rarefaction curves
In order to observe the sampling depth of each samples we start by plotting rarefactions curves. Those plots are generated by Plotly which makes the plots interactive.
rarefaction(data, "souche_temps", 100 )## rarefying sample SB1-Sauv0
## rarefying sample SB10-Mut0
## rarefying sample SB11-Mut0
## rarefying sample SB12-Mut0
## rarefying sample SB13-Sauv50
## rarefying sample SB14-Sauv50
## rarefying sample SB15-Sauv50
## rarefying sample SB16-Sauv50
## rarefying sample SB17-Sauv50
## rarefying sample SB18-Sauv50
## rarefying sample SB19-Mut50
## rarefying sample SB2-Sauv0
## rarefying sample SB20-Mut50
## rarefying sample SB21-Mut50
## rarefying sample SB22-Mut50
## rarefying sample SB23-Mut50
## rarefying sample SB24-Mut50
## rarefying sample SB3-Sauv0
## rarefying sample SB4-Sauv0
## rarefying sample SB5-Sauv0
## rarefying sample SB6-Sauv0
## rarefying sample SB7-Mut0
## rarefying sample SB8-Mut0
## rarefying sample SB9-Mut0## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.1.8.2 Composition plots
Composition plots reveals here the top 10 genus present in our samples. #TODO Ord1 option control the… Fact1 option control the…
1.8.2.1 Relative abundance
bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = TRUE)1.8.2.2 Raw abundance
bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = FALSE)1.8.3 Diversity analyses
1.8.3.1 Alpha diversity
This function computes various alpha diversity indexes and returns
alpha <- diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
column3 = "", supcovs = "", measures = c("Observed") )## INFO [2020-08-19 11:42:44] Alpha diversity tab ...
## INFO [2020-08-19 11:42:44] Done.
## INFO [2020-08-19 11:42:44] Plotting ...
## INFO [2020-08-19 11:42:44] Done.## INFO [2020-08-19 11:42:45] ANOVA ...
## INFO [2020-08-19 11:42:45] Done.
## INFO [2020-08-19 11:42:45] Finish.1.8.3.1.1 Tables
DT::datatable(alpha$alphatable, filter = "top")1.8.3.2 Beta diversity
beta <- diversity_beta_fun(data = data, output = "./plot_div_beta/", glom = "ASV", column1 = "temps", column2 = "souche", covar ="")## INFO [2020-08-19 11:42:45] Option1...
## [1] "t0" "t50"
## INFO [2020-08-19 11:42:45] Split table t0...
## INFO [2020-08-19 11:42:45] Done.
## [1] ""
## INFO [2020-08-19 11:42:45] No glom ...
## INFO [2020-08-19 11:42:45] Bray ...
##
## mutant sauvage
## 6 6
## INFO [2020-08-19 11:42:46] Done
## INFO [2020-08-19 11:42:46] Unifrac ...
## INFO [2020-08-19 11:42:46] Done
## INFO [2020-08-19 11:42:46] wunifrac ...
## INFO [2020-08-19 11:42:46] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.53973 0.53973 2.8355 0.17954 0.022977 *
## souche 1 0.75338 0.75338 3.9580 0.25061 0.004995 **
## Residuals 9 1.71311 0.19035 0.56985
## Total 11 3.00623 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.952842 4.640344 0.3169559 0.006 0.006 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.12045 0.120447 1.6362 0.12272 0.13487
## souche 1 0.19850 0.198504 2.6965 0.20225 0.01099 *
## Residuals 9 0.66253 0.073615 0.67503
## Total 11 0.98148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.2429196 3.289082 0.2475026 0.004 0.004 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.51694 0.51694 5.3962 0.32059 0.003996 **
## souche 1 0.23337 0.23337 2.4360 0.14472 0.059940 .
## Residuals 9 0.86218 0.09580 0.53469
## Total 11 1.61249 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.3815338 3.099498 0.236612 0.034 0.034 .
## INFO [2020-08-19 11:42:46] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383252
## Run 1 stress 0.1383248
## ... New best solution
## ... Procrustes: rmse 0.0006325115 max resid 0.001417263
## ... Similar to previous best
## Run 2 stress 0.1383252
## ... Procrustes: rmse 0.0002779707 max resid 0.0004883743
## ... Similar to previous best
## Run 3 stress 0.1383247
## ... New best solution
## ... Procrustes: rmse 9.798232e-05 max resid 0.0002303137
## ... Similar to previous best
## Run 4 stress 0.1383256
## ... Procrustes: rmse 0.0006924759 max resid 0.001227627
## ... Similar to previous best
## Run 5 stress 0.1383253
## ... Procrustes: rmse 0.0004508255 max resid 0.001080641
## ... Similar to previous best
## Run 6 stress 0.1415936
## Run 7 stress 0.1383251
## ... Procrustes: rmse 0.000165084 max resid 0.0003994996
## ... Similar to previous best
## Run 8 stress 0.2147072
## Run 9 stress 0.1416607
## Run 10 stress 0.1471255
## Run 11 stress 0.2163916
## Run 12 stress 0.1416007
## Run 13 stress 0.1415954
## Run 14 stress 0.1415955
## Run 15 stress 0.1415947
## Run 16 stress 0.1415941
## Run 17 stress 0.2308366
## Run 18 stress 0.214706
## Run 19 stress 0.1383248
## ... Procrustes: rmse 5.447613e-05 max resid 0.0001091228
## ... Similar to previous best
## Run 20 stress 0.1383249
## ... Procrustes: rmse 0.0003649483 max resid 0.0006793969
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383248
## Run 1 stress 0.1415925
## Run 2 stress 0.1415973
## Run 3 stress 0.1415955
## Run 4 stress 0.1383247
## ... New best solution
## ... Procrustes: rmse 0.0002484523 max resid 0.0005541465
## ... Similar to previous best
## Run 5 stress 0.138325
## ... Procrustes: rmse 0.0003688702 max resid 0.0007638162
## ... Similar to previous best
## Run 6 stress 0.1383256
## ... Procrustes: rmse 0.0007748377 max resid 0.001373165
## ... Similar to previous best
## Run 7 stress 0.1415974
## Run 8 stress 0.141612
## Run 9 stress 0.1383249
## ... Procrustes: rmse 0.0002320819 max resid 0.0004714879
## ... Similar to previous best
## Run 10 stress 0.1415921
## Run 11 stress 0.1383249
## ... Procrustes: rmse 0.0003083611 max resid 0.0005970506
## ... Similar to previous best
## Run 12 stress 0.138327
## ... Procrustes: rmse 0.001259103 max resid 0.002256352
## ... Similar to previous best
## Run 13 stress 0.1383258
## ... Procrustes: rmse 0.000839465 max resid 0.001480589
## ... Similar to previous best
## Run 14 stress 0.1415938
## Run 15 stress 0.1415932
## Run 16 stress 0.1471255
## Run 17 stress 0.2217286
## Run 18 stress 0.1416426
## Run 19 stress 0.2214244
## Run 20 stress 0.1471256
## *** Solution reached
## Run 0 stress 0.1396049
## Run 1 stress 0.1554507
## Run 2 stress 0.1396049
## ... New best solution
## ... Procrustes: rmse 2.255282e-05 max resid 4.666725e-05
## ... Similar to previous best
## Run 3 stress 0.1396049
## ... Procrustes: rmse 2.672727e-05 max resid 5.696865e-05
## ... Similar to previous best
## Run 4 stress 0.139605
## ... Procrustes: rmse 0.0002209797 max resid 0.0004719349
## ... Similar to previous best
## Run 5 stress 0.1554503
## Run 6 stress 0.2004091
## Run 7 stress 0.2597856
## Run 8 stress 0.3237913
## Run 9 stress 0.1396049
## ... Procrustes: rmse 0.0001302444 max resid 0.0002794703
## ... Similar to previous best
## Run 10 stress 0.3359247
## Run 11 stress 0.139605
## ... Procrustes: rmse 0.0001271679 max resid 0.0002570252
## ... Similar to previous best
## Run 12 stress 0.1396049
## ... Procrustes: rmse 2.396927e-05 max resid 5.139241e-05
## ... Similar to previous best
## Run 13 stress 0.1554504
## Run 14 stress 0.2265774
## Run 15 stress 0.1396049
## ... Procrustes: rmse 8.842138e-05 max resid 0.000186525
## ... Similar to previous best
## Run 16 stress 0.1976767
## Run 17 stress 0.1554504
## Run 18 stress 0.3359627
## Run 19 stress 0.1396049
## ... Procrustes: rmse 0.0001482711 max resid 0.0003181528
## ... Similar to previous best
## Run 20 stress 0.1396049
## ... Procrustes: rmse 1.99135e-05 max resid 4.104833e-05
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.04595665
## Run 1 stress 0.04595686
## ... Procrustes: rmse 4.794641e-05 max resid 0.0001040953
## ... Similar to previous best
## Run 2 stress 0.0459568
## ... Procrustes: rmse 4.159935e-05 max resid 0.0001000835
## ... Similar to previous best
## Run 3 stress 0.08294085
## Run 4 stress 0.08256847
## Run 5 stress 0.05106926
## Run 6 stress 0.05106757
## Run 7 stress 0.0510631
## Run 8 stress 0.05318672
## Run 9 stress 0.04595582
## ... New best solution
## ... Procrustes: rmse 0.000895018 max resid 0.002195193
## ... Similar to previous best
## Run 10 stress 0.08256776
## Run 11 stress 0.08157745
## Run 12 stress 0.05318842
## Run 13 stress 0.05318129
## Run 14 stress 0.08157773
## Run 15 stress 0.08157383
## Run 16 stress 0.0483876
## Run 17 stress 0.0825701
## Run 18 stress 0.0837327
## Run 19 stress 0.04595726
## ... Procrustes: rmse 0.0004611338 max resid 0.001114228
## ... Similar to previous best
## Run 20 stress 0.08158127
## *** Solution reached
## INFO [2020-08-19 11:42:47] Done.
## INFO [2020-08-19 11:42:47] Saving ...## INFO [2020-08-19 11:42:49] Supplement Beta plots ...
## INFO [2020-08-19 11:42:49] Done.
## INFO [2020-08-19 11:42:49] Split table t50...
## INFO [2020-08-19 11:42:49] Done.
## [1] ""
## INFO [2020-08-19 11:42:49] No glom ...
## INFO [2020-08-19 11:42:49] Bray ...
##
## mutant sauvage
## 6 6
## INFO [2020-08-19 11:42:49] Done
## INFO [2020-08-19 11:42:49] Unifrac ...
## INFO [2020-08-19 11:42:49] Done
## INFO [2020-08-19 11:42:49] wunifrac ...
## INFO [2020-08-19 11:42:49] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.06369 0.06369 3.118 0.03093 0.113886
## souche 1 1.81185 1.81185 88.707 0.87981 0.000999 ***
## Residuals 9 0.18383 0.02043 0.08926
## Total 11 2.05937 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.817719 75.21929 0.8826557 0.003 0.003 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.08586 0.08586 7.227 0.08985 0.006993 **
## souche 1 0.76280 0.76280 64.202 0.79825 0.000999 ***
## Residuals 9 0.10693 0.01188 0.11190
## Total 11 0.95559 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.7648939 40.10959 0.8004374 0.002 0.002 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.01031 0.01031 2.114 0.01920 0.170829
## souche 1 0.48297 0.48297 98.982 0.89905 0.000999 ***
## Residuals 9 0.04391 0.00488 0.08175
## Total 11 0.53720 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.4854355 93.77947 0.9036418 0.004 0.004 *
## INFO [2020-08-19 11:42:49] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 7.297422e-05
## Run 1 stress 0.2334455
## Run 2 stress 0.2444045
## Run 3 stress 0.3069117
## Run 4 stress 9.815733e-05
## ... Procrustes: rmse 0.0002642253 max resid 0.0006085487
## ... Similar to previous best
## Run 5 stress 9.466169e-05
## ... Procrustes: rmse 0.0002364957 max resid 0.0005860717
## ... Similar to previous best
## Run 6 stress 9.810564e-05
## ... Procrustes: rmse 0.0001191326 max resid 0.0002410768
## ... Similar to previous best
## Run 7 stress 9.54298e-05
## ... Procrustes: rmse 8.12396e-05 max resid 0.0001787486
## ... Similar to previous best
## Run 8 stress 9.386827e-05
## ... Procrustes: rmse 0.0001338526 max resid 0.0002820177
## ... Similar to previous best
## Run 9 stress 9.429826e-05
## ... Procrustes: rmse 0.000225061 max resid 0.000565046
## ... Similar to previous best
## Run 10 stress 9.159822e-05
## ... Procrustes: rmse 0.0002320587 max resid 0.0005398254
## ... Similar to previous best
## Run 11 stress 9.909955e-05
## ... Procrustes: rmse 0.0001134436 max resid 0.0002162572
## ... Similar to previous best
## Run 12 stress 8.915087e-05
## ... Procrustes: rmse 7.946781e-05 max resid 0.0002197547
## ... Similar to previous best
## Run 13 stress 9.370447e-05
## ... Procrustes: rmse 9.325583e-05 max resid 0.0002250259
## ... Similar to previous best
## Run 14 stress 9.683309e-05
## ... Procrustes: rmse 7.282874e-05 max resid 0.0001491394
## ... Similar to previous best
## Run 15 stress 9.249103e-05
## ... Procrustes: rmse 8.132925e-05 max resid 0.0002212376
## ... Similar to previous best
## Run 16 stress 9.346381e-05
## ... Procrustes: rmse 8.871461e-05 max resid 0.0002397497
## ... Similar to previous best
## Run 17 stress 8.81122e-05
## ... Procrustes: rmse 7.927522e-05 max resid 0.0001150675
## ... Similar to previous best
## Run 18 stress 8.411536e-05
## ... Procrustes: rmse 0.0002189546 max resid 0.0005114147
## ... Similar to previous best
## Run 19 stress 9.923089e-05
## ... Procrustes: rmse 0.0002622837 max resid 0.0006037906
## ... Similar to previous best
## Run 20 stress 0.233779
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 8.694326e-05
## Run 1 stress 9.7571e-05
## ... Procrustes: rmse 2.871858e-05 max resid 5.893487e-05
## ... Similar to previous best
## Run 2 stress 9.156634e-05
## ... Procrustes: rmse 0.0002512312 max resid 0.0006491876
## ... Similar to previous best
## Run 3 stress 8.967182e-05
## ... Procrustes: rmse 6.22289e-05 max resid 0.0001626015
## ... Similar to previous best
## Run 4 stress 9.215938e-05
## ... Procrustes: rmse 7.398548e-05 max resid 0.0001671002
## ... Similar to previous best
## Run 5 stress 9.377216e-05
## ... Procrustes: rmse 0.0002407549 max resid 0.0006674717
## ... Similar to previous best
## Run 6 stress 0.2288378
## Run 7 stress 9.596657e-05
## ... Procrustes: rmse 6.581733e-05 max resid 0.0001652081
## ... Similar to previous best
## Run 8 stress 9.656764e-05
## ... Procrustes: rmse 0.0002463088 max resid 0.0006818128
## ... Similar to previous best
## Run 9 stress 9.542873e-05
## ... Procrustes: rmse 2.596822e-05 max resid 5.253937e-05
## ... Similar to previous best
## Run 10 stress 7.987219e-05
## ... New best solution
## ... Procrustes: rmse 0.0002259861 max resid 0.0005875277
## ... Similar to previous best
## Run 11 stress 9.787269e-05
## ... Procrustes: rmse 0.000171325 max resid 0.0005194162
## ... Similar to previous best
## Run 12 stress 9.546432e-05
## ... Procrustes: rmse 0.0002346911 max resid 0.0006103371
## ... Similar to previous best
## Run 13 stress 9.82409e-05
## ... Procrustes: rmse 0.0001733348 max resid 0.0005260992
## ... Similar to previous best
## Run 14 stress 9.189414e-05
## ... Procrustes: rmse 0.0002200697 max resid 0.000611242
## ... Similar to previous best
## Run 15 stress 9.309562e-05
## ... Procrustes: rmse 0.0002319344 max resid 0.0006015431
## ... Similar to previous best
## Run 16 stress 8.067644e-05
## ... Procrustes: rmse 0.0002224298 max resid 0.0005708495
## ... Similar to previous best
## Run 17 stress 9.738121e-05
## ... Procrustes: rmse 0.0001826412 max resid 0.0003705574
## ... Similar to previous best
## Run 18 stress 9.44268e-05
## ... Procrustes: rmse 0.0001971326 max resid 0.0004378512
## ... Similar to previous best
## Run 19 stress 7.276678e-05
## ... New best solution
## ... Procrustes: rmse 0.0002095399 max resid 0.000564796
## ... Similar to previous best
## Run 20 stress 9.663297e-05
## ... Procrustes: rmse 0.000201736 max resid 0.0006023578
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 9.633728e-05
## Run 1 stress 9.533088e-05
## ... New best solution
## ... Procrustes: rmse 0.000178975 max resid 0.0003245182
## ... Similar to previous best
## Run 2 stress 0.2407971
## Run 3 stress 8.464239e-05
## ... New best solution
## ... Procrustes: rmse 0.0002079414 max resid 0.0006492518
## ... Similar to previous best
## Run 4 stress 8.87606e-05
## ... Procrustes: rmse 0.0001284124 max resid 0.0002283617
## ... Similar to previous best
## Run 5 stress 9.574923e-05
## ... Procrustes: rmse 0.0001637072 max resid 0.0003062904
## ... Similar to previous best
## Run 6 stress 9.996618e-05
## ... Procrustes: rmse 0.0001796929 max resid 0.0003230694
## ... Similar to previous best
## Run 7 stress 9.501575e-05
## ... Procrustes: rmse 0.0001621139 max resid 0.0002966499
## ... Similar to previous best
## Run 8 stress 9.712025e-05
## ... Procrustes: rmse 0.0001503913 max resid 0.0002906577
## ... Similar to previous best
## Run 9 stress 9.514845e-05
## ... Procrustes: rmse 0.0001581236 max resid 0.0002916892
## ... Similar to previous best
## Run 10 stress 9.249757e-05
## ... Procrustes: rmse 0.0001995491 max resid 0.000622048
## ... Similar to previous best
## Run 11 stress 8.211476e-05
## ... New best solution
## ... Procrustes: rmse 0.000135033 max resid 0.0002298485
## ... Similar to previous best
## Run 12 stress 9.168225e-05
## ... Procrustes: rmse 0.0001485928 max resid 0.0002177646
## ... Similar to previous best
## Run 13 stress 8.864717e-05
## ... Procrustes: rmse 0.0001293885 max resid 0.0002082727
## ... Similar to previous best
## Run 14 stress 9.177623e-05
## ... Procrustes: rmse 0.0001214138 max resid 0.0002339482
## ... Similar to previous best
## Run 15 stress 9.530143e-05
## ... Procrustes: rmse 0.0001100754 max resid 0.0002382435
## ... Similar to previous best
## Run 16 stress 0.3023067
## Run 17 stress 0.3355958
## Run 18 stress 9.362259e-05
## ... Procrustes: rmse 0.0001483153 max resid 0.0002641358
## ... Similar to previous best
## Run 19 stress 9.206595e-05
## ... Procrustes: rmse 0.0001338575 max resid 0.0002391863
## ... Similar to previous best
## Run 20 stress 9.863644e-05
## ... Procrustes: rmse 0.0001404396 max resid 0.0002415025
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.001774538
## Run 1 stress 9.93138e-05
## ... New best solution
## ... Procrustes: rmse 0.01016925 max resid 0.01925855
## Run 2 stress 0.004372292
## Run 3 stress 0.0001985273
## ... Procrustes: rmse 0.0005914205 max resid 0.001120358
## ... Similar to previous best
## Run 4 stress 9.972073e-05
## ... Procrustes: rmse 0.0002558317 max resid 0.0004699184
## ... Similar to previous best
## Run 5 stress 0.0005051606
## ... Procrustes: rmse 0.002498253 max resid 0.004586508
## ... Similar to previous best
## Run 6 stress 0.001107762
## Run 7 stress 0.0002511515
## ... Procrustes: rmse 0.001004284 max resid 0.001713014
## ... Similar to previous best
## Run 8 stress 0.0001881088
## ... Procrustes: rmse 0.0006493462 max resid 0.001258036
## ... Similar to previous best
## Run 9 stress 0.3298258
## Run 10 stress 0.0003399524
## ... Procrustes: rmse 0.001519312 max resid 0.002712545
## ... Similar to previous best
## Run 11 stress 0.001735692
## Run 12 stress 0.0002769074
## ... Procrustes: rmse 0.0005731094 max resid 0.0009233264
## ... Similar to previous best
## Run 13 stress 0.001273921
## Run 14 stress 0.0004609634
## ... Procrustes: rmse 0.002171964 max resid 0.004114243
## ... Similar to previous best
## Run 15 stress 0.0002006846
## ... Procrustes: rmse 0.0007126281 max resid 0.001307296
## ... Similar to previous best
## Run 16 stress 0.001248139
## Run 17 stress 0.00155523
## Run 18 stress 0.0008136382
## Run 19 stress 0.0003453806
## ... Procrustes: rmse 0.001477394 max resid 0.002797753
## ... Similar to previous best
## Run 20 stress 0.003049723
## *** Solution reached
## INFO [2020-08-19 11:42:50] Done.
## INFO [2020-08-19 11:42:50] Saving ...
## INFO [2020-08-19 11:42:52] Supplement Beta plots ...
## INFO [2020-08-19 11:42:52] Done.
## INFO [2020-08-19 11:42:52] Global1...
## [1] ""
## INFO [2020-08-19 11:42:52] No glom ...
## INFO [2020-08-19 11:42:52] Bray ...
## souche
## temps mutant sauvage
## t0 6 6
## t50 6 6
## INFO [2020-08-19 11:42:53] Done
## INFO [2020-08-19 11:42:53] Unifrac ...
## INFO [2020-08-19 11:42:53] Done
## INFO [2020-08-19 11:42:53] wunifrac ...
## INFO [2020-08-19 11:42:53] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 3.1218 0.06845 0.020979 *
## temps 1 2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche 1 1.4711 1.47112 9.0493 0.19841 0.000999 ***
## Residuals 20 3.2514 0.16257 0.43851
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.952842 4.640344 0.3169559 0.004 0.0048
## 2 t0-sauvage vs t50-sauvage 1 2.020676 28.967360 0.7433750 0.002 0.0040
## 3 t0-sauvage vs t50-mutant 1 2.197269 26.004113 0.7222540 0.001 0.0030
## 4 t0-mutant vs t50-sauvage 1 1.680832 11.591365 0.5368519 0.006 0.0060
## 5 t0-mutant vs t50-mutant 1 1.569713 9.826226 0.4956176 0.001 0.0030
## 6 t50-sauvage vs t50-mutant 1 1.817719 75.219295 0.8826557 0.004 0.0048
## sig
## 1 *
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14326 0.14326 2.4262 0.04787 0.032967 *
## temps 1 1.03837 1.03837 17.5862 0.34698 0.000999 ***
## souche 1 0.63007 0.63007 10.6711 0.21054 0.000999 ***
## Residuals 20 1.18089 0.05904 0.39460
## Total 23 2.99260 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.2378044 3.318044 0.2491390 0.001 0.0040
## 2 t0-sauvage vs t50-sauvage 1 0.5256437 11.749607 0.5402216 0.004 0.0048
## 3 t0-sauvage vs t50-mutant 1 1.0643999 21.792240 0.6854578 0.002 0.0040
## 4 t0-mutant vs t50-sauvage 1 0.6364616 14.416735 0.5904448 0.003 0.0045
## 5 t0-mutant vs t50-mutant 1 0.8835482 18.310605 0.6467755 0.002 0.0040
## 6 t50-sauvage vs t50-mutant 1 0.7775266 36.468644 0.7848011 0.006 0.0060
## sig
## 1 *
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.21699 0.21699 6.4192 0.14472 0.001998 **
## temps 1 0.35230 0.35230 10.4221 0.23497 0.000999 ***
## souche 1 0.25400 0.25400 7.5142 0.16941 0.000999 ***
## Residuals 20 0.67606 0.03380 0.45090
## Total 23 1.49935 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.1634340 2.838825 0.2211125 0.052 0.0520
## 2 t0-sauvage vs t50-sauvage 1 0.3595478 13.063956 0.5664230 0.002 0.0060
## 3 t0-sauvage vs t50-mutant 1 0.4692174 15.571734 0.6089432 0.004 0.0060
## 4 t0-mutant vs t50-sauvage 1 0.2136786 6.791884 0.4044742 0.006 0.0072
## 5 t0-mutant vs t50-mutant 1 0.2555299 7.499838 0.4285662 0.004 0.0060
## 6 t50-sauvage vs t50-mutant 1 0.3054314 75.930639 0.8836271 0.002 0.0060
## sig
## 1
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
## INFO [2020-08-19 11:42:54] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1278079
## Run 2 stress 0.1004882
## ... Procrustes: rmse 0.00582239 max resid 0.02234147
## Run 3 stress 0.127817
## Run 4 stress 0.1282047
## Run 5 stress 0.1004882
## ... Procrustes: rmse 0.005801538 max resid 0.02225939
## Run 6 stress 0.1282053
## Run 7 stress 0.1316113
## Run 8 stress 0.1004882
## ... Procrustes: rmse 0.005811 max resid 0.02233573
## Run 9 stress 0.1004882
## ... Procrustes: rmse 0.005799686 max resid 0.02229184
## Run 10 stress 0.138303
## Run 11 stress 0.1004882
## ... Procrustes: rmse 0.005810812 max resid 0.02230361
## Run 12 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 2.234472e-06 max resid 5.37307e-06
## ... Similar to previous best
## Run 13 stress 0.1278188
## Run 14 stress 0.1282049
## Run 15 stress 0.1282048
## Run 16 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 9.679456e-06 max resid 2.799009e-05
## ... Similar to previous best
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.005832762 max resid 0.02235717
## Run 18 stress 0.1004882
## ... Procrustes: rmse 0.005822964 max resid 0.02233735
## Run 19 stress 0.131635
## Run 20 stress 0.1004882
## ... Procrustes: rmse 0.005825946 max resid 0.02234164
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1004882
## ... Procrustes: rmse 2.561791e-05 max resid 8.129794e-05
## ... Similar to previous best
## Run 2 stress 0.1004765
## ... New best solution
## ... Procrustes: rmse 0.005817267 max resid 0.02226881
## Run 3 stress 0.1278241
## Run 4 stress 0.1351097
## Run 5 stress 0.1282049
## Run 6 stress 0.2431576
## Run 7 stress 0.1004765
## ... Procrustes: rmse 0.0001663419 max resid 0.0004631308
## ... Similar to previous best
## Run 8 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 6.287301e-05 max resid 0.0001670129
## ... Similar to previous best
## Run 9 stress 0.1004764
## ... Procrustes: rmse 7.207364e-05 max resid 0.0001870077
## ... Similar to previous best
## Run 10 stress 0.1316112
## Run 11 stress 0.1278165
## Run 12 stress 0.1004764
## ... Procrustes: rmse 9.325252e-06 max resid 1.725324e-05
## ... Similar to previous best
## Run 13 stress 0.1004764
## ... Procrustes: rmse 4.245998e-05 max resid 0.0001149144
## ... Similar to previous best
## Run 14 stress 0.1004765
## ... Procrustes: rmse 0.0001360717 max resid 0.0004412301
## ... Similar to previous best
## Run 15 stress 0.1322826
## Run 16 stress 0.1332824
## Run 17 stress 0.1004764
## ... Procrustes: rmse 1.032627e-05 max resid 2.185507e-05
## ... Similar to previous best
## Run 18 stress 0.1004764
## ... Procrustes: rmse 2.070388e-05 max resid 5.152314e-05
## ... Similar to previous best
## Run 19 stress 0.1004764
## ... Procrustes: rmse 1.307659e-05 max resid 3.520137e-05
## ... Similar to previous best
## Run 20 stress 0.127819
## *** Solution reached
## Run 0 stress 0.1240701
## Run 1 stress 0.1242994
## ... Procrustes: rmse 0.00790244 max resid 0.03076246
## Run 2 stress 0.1240701
## ... New best solution
## ... Procrustes: rmse 2.43171e-06 max resid 8.415182e-06
## ... Similar to previous best
## Run 3 stress 0.1240701
## ... New best solution
## ... Procrustes: rmse 5.616045e-06 max resid 2.097401e-05
## ... Similar to previous best
## Run 4 stress 0.1249445
## Run 5 stress 0.171959
## Run 6 stress 0.1742561
## Run 7 stress 0.1240701
## ... Procrustes: rmse 5.193758e-06 max resid 1.730566e-05
## ... Similar to previous best
## Run 8 stress 0.1240701
## ... Procrustes: rmse 8.135601e-06 max resid 2.580401e-05
## ... Similar to previous best
## Run 9 stress 0.1240701
## ... Procrustes: rmse 1.113284e-05 max resid 3.98475e-05
## ... Similar to previous best
## Run 10 stress 0.125379
## Run 11 stress 0.1249445
## Run 12 stress 0.1240701
## ... Procrustes: rmse 3.679917e-06 max resid 1.134398e-05
## ... Similar to previous best
## Run 13 stress 0.1238509
## ... New best solution
## ... Procrustes: rmse 0.01402275 max resid 0.05064204
## Run 14 stress 0.125379
## Run 15 stress 0.1242988
## ... Procrustes: rmse 0.01576687 max resid 0.05033526
## Run 16 stress 0.1249448
## Run 17 stress 0.1240702
## ... Procrustes: rmse 0.01402352 max resid 0.05072772
## Run 18 stress 0.1240701
## ... Procrustes: rmse 0.01402285 max resid 0.05072296
## Run 19 stress 0.125379
## Run 20 stress 0.125379
## *** No convergence -- monoMDS stopping criteria:
## 17: stress ratio > sratmax
## 3: scale factor of the gradient < sfgrmin
## Run 0 stress 0.07641955
## Run 1 stress 0.07642016
## ... Procrustes: rmse 0.0005874296 max resid 0.001699592
## ... Similar to previous best
## Run 2 stress 0.08113255
## Run 3 stress 0.0810459
## Run 4 stress 0.08122861
## Run 5 stress 0.08122864
## Run 6 stress 0.08113645
## Run 7 stress 0.08122922
## Run 8 stress 0.07683517
## ... Procrustes: rmse 0.009197378 max resid 0.03779424
## Run 9 stress 0.08122852
## Run 10 stress 0.09530016
## Run 11 stress 0.08122904
## Run 12 stress 0.0764211
## ... Procrustes: rmse 0.0004631289 max resid 0.001299319
## ... Similar to previous best
## Run 13 stress 0.07683559
## ... Procrustes: rmse 0.009307862 max resid 0.03846817
## Run 14 stress 0.08074994
## Run 15 stress 0.08074989
## Run 16 stress 0.08074988
## Run 17 stress 0.09858413
## Run 18 stress 0.08122863
## Run 19 stress 0.08104587
## Run 20 stress 0.08122961
## *** Solution reached
## INFO [2020-08-19 11:42:55] Done.
## INFO [2020-08-19 11:42:55] Saving ...
## INFO [2020-08-19 11:42:57] Supplement Beta plots ...
## INFO [2020-08-19 11:42:57] Done.
## INFO [2020-08-19 11:42:57] Global2...
## [1] ""
## INFO [2020-08-19 11:42:57] No glom ...
## INFO [2020-08-19 11:42:57] Bray ...
##
## t0 t50
## 12 12
## INFO [2020-08-19 11:42:57] Done
## INFO [2020-08-19 11:42:57] Unifrac ...
## INFO [2020-08-19 11:42:57] Done
## INFO [2020-08-19 11:42:57] wunifrac ...
## INFO [2020-08-19 11:42:58] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 2.2568 0.06845 0.043956 *
## temps 1 2.1846 2.18458 9.7144 0.29463 0.000999 ***
## Residuals 21 4.7225 0.22488 0.63692
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 2.348965 10.20159 0.316804 0.001 0.001 **
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14627 0.14627 1.6469 0.04771 0.166833
## temps 1 1.05433 1.05433 11.8715 0.34392 0.000999 ***
## Residuals 21 1.86505 0.08881 0.60837
## Total 23 3.06565 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 1.064335 11.70001 0.3471812 0.001 0.001 **
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.19527 0.19527 4.7847 0.14237 0.004995 **
## temps 1 0.31930 0.31930 7.8237 0.23279 0.000999 ***
## Residuals 21 0.85705 0.04081 0.62484
## Total 23 1.37163 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 0.3740116 8.247895 0.2726767 0.001 0.001 **
## INFO [2020-08-19 11:42:58] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1004764
## ... Procrustes: rmse 1.460734e-05 max resid 4.148648e-05
## ... Similar to previous best
## Run 2 stress 0.1278253
## Run 3 stress 0.1322863
## Run 4 stress 0.1282052
## Run 5 stress 0.1332149
## Run 6 stress 0.1004882
## ... Procrustes: rmse 0.005834734 max resid 0.02240601
## Run 7 stress 0.1278145
## Run 8 stress 0.1316113
## Run 9 stress 0.1332168
## Run 10 stress 0.1004764
## ... Procrustes: rmse 7.246805e-05 max resid 0.0001909761
## ... Similar to previous best
## Run 11 stress 0.1004882
## ... Procrustes: rmse 0.005820094 max resid 0.02233923
## Run 12 stress 0.1332148
## Run 13 stress 0.13229
## Run 14 stress 0.1004764
## ... Procrustes: rmse 1.053394e-05 max resid 2.279406e-05
## ... Similar to previous best
## Run 15 stress 0.1274964
## Run 16 stress 0.1004764
## ... Procrustes: rmse 3.415503e-05 max resid 9.322872e-05
## ... Similar to previous best
## Run 17 stress 0.1332825
## Run 18 stress 0.1004764
## ... Procrustes: rmse 1.31862e-05 max resid 4.616163e-05
## ... Similar to previous best
## Run 19 stress 0.1004882
## ... Procrustes: rmse 0.005806592 max resid 0.02229737
## Run 20 stress 0.1278118
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1278272
## Run 2 stress 0.1004882
## ... New best solution
## ... Procrustes: rmse 6.819145e-06 max resid 1.454001e-05
## ... Similar to previous best
## Run 3 stress 0.1282047
## Run 4 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 0.005815289 max resid 0.02232781
## Run 5 stress 0.1004882
## ... Procrustes: rmse 0.005819447 max resid 0.02236284
## Run 6 stress 0.1282048
## Run 7 stress 0.1004883
## ... Procrustes: rmse 0.005841318 max resid 0.02240352
## Run 8 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 6.266375e-05 max resid 0.0001718134
## ... Similar to previous best
## Run 9 stress 0.1004764
## ... Procrustes: rmse 2.719159e-06 max resid 5.941329e-06
## ... Similar to previous best
## Run 10 stress 0.1004882
## ... Procrustes: rmse 0.005839821 max resid 0.02239883
## Run 11 stress 0.1282046
## Run 12 stress 0.1004882
## ... Procrustes: rmse 0.005807723 max resid 0.0222973
## Run 13 stress 0.1332835
## Run 14 stress 0.1282046
## Run 15 stress 0.1004882
## ... Procrustes: rmse 0.005818327 max resid 0.02232324
## Run 16 stress 0.1004882
## ... Procrustes: rmse 0.005804974 max resid 0.02231282
## Run 17 stress 0.1282048
## Run 18 stress 0.1004882
## ... Procrustes: rmse 0.005822381 max resid 0.02233384
## Run 19 stress 0.1282046
## Run 20 stress 0.1004765
## ... Procrustes: rmse 7.581458e-05 max resid 0.0001973484
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1256803
## Run 1 stress 0.1249249
## ... New best solution
## ... Procrustes: rmse 0.01684669 max resid 0.0457718
## Run 2 stress 0.3725376
## Run 3 stress 0.1249249
## ... New best solution
## ... Procrustes: rmse 1.876361e-05 max resid 4.157707e-05
## ... Similar to previous best
## Run 4 stress 0.1268287
## Run 5 stress 0.1698976
## Run 6 stress 0.1249249
## ... Procrustes: rmse 3.354137e-05 max resid 9.441521e-05
## ... Similar to previous best
## Run 7 stress 0.1249249
## ... Procrustes: rmse 2.540111e-06 max resid 5.484595e-06
## ... Similar to previous best
## Run 8 stress 0.1268271
## Run 9 stress 0.1249058
## ... New best solution
## ... Procrustes: rmse 0.01342379 max resid 0.04855629
## Run 10 stress 0.1263106
## Run 11 stress 0.1249058
## ... Procrustes: rmse 1.359932e-05 max resid 3.813875e-05
## ... Similar to previous best
## Run 12 stress 0.1262165
## Run 13 stress 0.1249249
## ... Procrustes: rmse 0.0134237 max resid 0.04848362
## Run 14 stress 0.1268278
## Run 15 stress 0.1249058
## ... Procrustes: rmse 7.025107e-06 max resid 1.214134e-05
## ... Similar to previous best
## Run 16 stress 0.1249058
## ... Procrustes: rmse 2.572412e-05 max resid 8.542448e-05
## ... Similar to previous best
## Run 17 stress 0.1249058
## ... Procrustes: rmse 1.454983e-05 max resid 3.567124e-05
## ... Similar to previous best
## Run 18 stress 0.1263106
## Run 19 stress 0.1262165
## Run 20 stress 0.1249058
## ... New best solution
## ... Procrustes: rmse 5.713133e-06 max resid 1.702337e-05
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.08612709
## Run 1 stress 0.08612721
## ... Procrustes: rmse 0.0001230758 max resid 0.0005015536
## ... Similar to previous best
## Run 2 stress 0.0861272
## ... Procrustes: rmse 6.26278e-05 max resid 0.0002270132
## ... Similar to previous best
## Run 3 stress 0.08612712
## ... Procrustes: rmse 6.316098e-05 max resid 0.0002613876
## ... Similar to previous best
## Run 4 stress 0.1284577
## Run 5 stress 0.2311373
## Run 6 stress 0.0861274
## ... Procrustes: rmse 9.682461e-05 max resid 0.0003439576
## ... Similar to previous best
## Run 7 stress 0.207305
## Run 8 stress 0.09418535
## Run 9 stress 0.08612712
## ... Procrustes: rmse 3.100432e-05 max resid 9.834706e-05
## ... Similar to previous best
## Run 10 stress 0.09418486
## Run 11 stress 0.09418437
## Run 12 stress 0.1284591
## Run 13 stress 0.09344909
## Run 14 stress 0.08612709
## ... Procrustes: rmse 2.062e-05 max resid 8.298303e-05
## ... Similar to previous best
## Run 15 stress 0.1312055
## Run 16 stress 0.09345045
## Run 17 stress 0.09418426
## Run 18 stress 0.2310506
## Run 19 stress 0.1290919
## Run 20 stress 0.1295872
## *** Solution reached
## INFO [2020-08-19 11:42:58] Done.
## INFO [2020-08-19 11:42:58] Saving ...
## INFO [2020-08-19 11:43:01] Supplement Beta plots ...
## INFO [2020-08-19 11:43:01] Done.
## INFO [2020-08-19 11:43:01] Global3...
## [1] ""
## INFO [2020-08-19 11:43:01] No glom ...
## INFO [2020-08-19 11:43:01] Bray ...
##
## mutant sauvage
## 12 12
## INFO [2020-08-19 11:43:01] Done
## INFO [2020-08-19 11:43:01] Unifrac ...
## INFO [2020-08-19 11:43:01] Done
## INFO [2020-08-19 11:43:01] wunifrac ...
## INFO [2020-08-19 11:43:01] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 1.9574 0.06845 0.073926 .
## souche 1 1.4622 1.46217 5.6393 0.19720 0.000999 ***
## Residuals 21 5.4449 0.25928 0.73435
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.529137 5.715979 0.2062341 0.001 0.001 **
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14451 0.14451 1.3558 0.04797 0.231768
## souche 1 0.62942 0.62942 5.9052 0.20895 0.003996 **
## Residuals 21 2.23836 0.10659 0.74308
## Total 23 3.01229 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.6656014 6.23995 0.2209618 0.002 0.002 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.11860 0.118601 3.0286 0.10077 0.012987 *
## souche 1 0.23596 0.235957 6.0253 0.20048 0.000999 ***
## Residuals 21 0.82238 0.039161 0.69875
## Total 23 1.17694 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.2365825 5.534929 0.2010148 0.001 0.001 **
## INFO [2020-08-19 11:43:01] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1004764
## ... Procrustes: rmse 1.154521e-05 max resid 3.53991e-05
## ... Similar to previous best
## Run 2 stress 0.1004885
## ... Procrustes: rmse 0.005819579 max resid 0.02231832
## Run 3 stress 0.1004882
## ... Procrustes: rmse 0.005803715 max resid 0.02229188
## Run 4 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 1.111054e-05 max resid 2.655167e-05
## ... Similar to previous best
## Run 5 stress 0.1004764
## ... Procrustes: rmse 5.750598e-05 max resid 0.0001510157
## ... Similar to previous best
## Run 6 stress 0.1004883
## ... Procrustes: rmse 0.005849704 max resid 0.02241124
## Run 7 stress 0.1004764
## ... Procrustes: rmse 1.056377e-05 max resid 2.600251e-05
## ... Similar to previous best
## Run 8 stress 0.1282046
## Run 9 stress 0.1004882
## ... Procrustes: rmse 0.005812432 max resid 0.02230539
## Run 10 stress 0.1004882
## ... Procrustes: rmse 0.005822984 max resid 0.02233264
## Run 11 stress 0.1282051
## Run 12 stress 0.1004765
## ... Procrustes: rmse 0.0001189496 max resid 0.0003012801
## ... Similar to previous best
## Run 13 stress 0.1004883
## ... Procrustes: rmse 0.005814722 max resid 0.02230269
## Run 14 stress 0.1004882
## ... Procrustes: rmse 0.005815698 max resid 0.02231601
## Run 15 stress 0.127582
## Run 16 stress 0.1004765
## ... Procrustes: rmse 0.0001249548 max resid 0.0003239086
## ... Similar to previous best
## Run 17 stress 0.1004764
## ... Procrustes: rmse 2.345706e-05 max resid 6.490324e-05
## ... Similar to previous best
## Run 18 stress 0.1004882
## ... Procrustes: rmse 0.005817773 max resid 0.02231451
## Run 19 stress 0.1004882
## ... Procrustes: rmse 0.005824295 max resid 0.02233978
## Run 20 stress 0.1332828
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1004765
## ... New best solution
## ... Procrustes: rmse 0.005816619 max resid 0.0222787
## Run 2 stress 0.1278281
## Run 3 stress 0.1004765
## ... New best solution
## ... Procrustes: rmse 2.684107e-05 max resid 7.043686e-05
## ... Similar to previous best
## Run 4 stress 0.1004882
## ... Procrustes: rmse 0.005827093 max resid 0.02231957
## Run 5 stress 0.1322818
## Run 6 stress 0.127607
## Run 7 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 7.477728e-05 max resid 0.0001950756
## ... Similar to previous best
## Run 8 stress 0.1004882
## ... Procrustes: rmse 0.00581747 max resid 0.02233454
## Run 9 stress 0.1004765
## ... Procrustes: rmse 6.324246e-05 max resid 0.00016833
## ... Similar to previous best
## Run 10 stress 0.1004882
## ... Procrustes: rmse 0.005799534 max resid 0.02228797
## Run 11 stress 0.1282046
## Run 12 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 1.49946e-05 max resid 4.893117e-05
## ... Similar to previous best
## Run 13 stress 0.1004765
## ... Procrustes: rmse 0.0001098263 max resid 0.0002917477
## ... Similar to previous best
## Run 14 stress 0.138419
## Run 15 stress 0.1004764
## ... Procrustes: rmse 1.039399e-05 max resid 1.978762e-05
## ... Similar to previous best
## Run 16 stress 0.1282045
## Run 17 stress 0.1004765
## ... Procrustes: rmse 8.70372e-05 max resid 0.0002393608
## ... Similar to previous best
## Run 18 stress 0.1004764
## ... Procrustes: rmse 2.25671e-05 max resid 5.544975e-05
## ... Similar to previous best
## Run 19 stress 0.1004764
## ... Procrustes: rmse 1.649469e-05 max resid 3.365621e-05
## ... Similar to previous best
## Run 20 stress 0.1004764
## ... Procrustes: rmse 7.09353e-06 max resid 1.812244e-05
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1237948
## Run 1 stress 0.1250086
## Run 2 stress 0.1242106
## ... Procrustes: rmse 0.0159052 max resid 0.04636032
## Run 3 stress 0.1242106
## ... Procrustes: rmse 0.01594085 max resid 0.04633947
## Run 4 stress 0.1242184
## ... Procrustes: rmse 0.009186847 max resid 0.03462367
## Run 5 stress 0.1250086
## Run 6 stress 0.1249325
## Run 7 stress 0.1252029
## Run 8 stress 0.1703687
## Run 9 stress 0.1237948
## ... New best solution
## ... Procrustes: rmse 0.0001119318 max resid 0.0004210171
## ... Similar to previous best
## Run 10 stress 0.1690862
## Run 11 stress 0.1237948
## ... Procrustes: rmse 9.501374e-05 max resid 0.0003572056
## ... Similar to previous best
## Run 12 stress 0.1250086
## Run 13 stress 0.1250086
## Run 14 stress 0.3458484
## Run 15 stress 0.1242106
## ... Procrustes: rmse 0.01592723 max resid 0.04638958
## Run 16 stress 0.1242188
## ... Procrustes: rmse 0.009446607 max resid 0.03574283
## Run 17 stress 0.1252047
## Run 18 stress 0.1250352
## Run 19 stress 0.1237849
## ... New best solution
## ... Procrustes: rmse 0.01244131 max resid 0.04504723
## Run 20 stress 0.1242184
## ... Procrustes: rmse 0.01531351 max resid 0.04395022
## *** No convergence -- monoMDS stopping criteria:
## 20: stress ratio > sratmax
## Run 0 stress 0.08622534
## Run 1 stress 0.08622531
## ... New best solution
## ... Procrustes: rmse 0.0001078813 max resid 0.0002978303
## ... Similar to previous best
## Run 2 stress 0.09017176
## Run 3 stress 0.08622528
## ... New best solution
## ... Procrustes: rmse 0.0001558791 max resid 0.0004687973
## ... Similar to previous best
## Run 4 stress 0.09017233
## Run 5 stress 0.1315239
## Run 6 stress 0.1315249
## Run 7 stress 0.09017144
## Run 8 stress 0.3842931
## Run 9 stress 0.08622526
## ... New best solution
## ... Procrustes: rmse 0.0001201927 max resid 0.000340439
## ... Similar to previous best
## Run 10 stress 0.1251127
## Run 11 stress 0.09017141
## Run 12 stress 0.08622542
## ... Procrustes: rmse 0.0001732654 max resid 0.0007008996
## ... Similar to previous best
## Run 13 stress 0.08622525
## ... New best solution
## ... Procrustes: rmse 3.245498e-05 max resid 9.169185e-05
## ... Similar to previous best
## Run 14 stress 0.08622524
## ... New best solution
## ... Procrustes: rmse 4.75078e-05 max resid 0.000167523
## ... Similar to previous best
## Run 15 stress 0.09097867
## Run 16 stress 0.1251195
## Run 17 stress 0.1287963
## Run 18 stress 0.08622524
## ... Procrustes: rmse 2.231465e-05 max resid 8.17788e-05
## ... Similar to previous best
## Run 19 stress 0.09017212
## Run 20 stress 0.09097896
## *** Solution reached
## INFO [2020-08-19 11:43:02] Done.
## INFO [2020-08-19 11:43:02] Saving ...
## INFO [2020-08-19 11:43:05] Supplement Beta plots ...
## INFO [2020-08-19 11:43:05] Done.
## INFO [2020-08-19 11:43:05] Finish